| vars | n | mean | sd | median | trimmed | mad | min | max | range | skew | kurtosis | se | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| climate.anger | 1 | 239 | 3.849372 | 0.9400684 | 4.00 | 3.950777 | 1.11195 | 1 | 5 | 4 | -0.8236903 | 0.0927332 | 0.0608080 |
| climate.contempt | 2 | 239 | 2.407950 | 1.0096174 | 2.25 | 2.323834 | 1.11195 | 1 | 5 | 4 | 0.6608964 | -0.0304711 | 0.0653067 |
| climate.enthusiasm | 3 | 239 | 3.599372 | 0.8169101 | 3.75 | 3.658031 | 0.74130 | 1 | 5 | 4 | -0.7574711 | 0.4252301 | 0.0528415 |
| climate.powerlessness | 4 | 239 | 3.283473 | 0.7771209 | 3.25 | 3.323834 | 0.74130 | 1 | 5 | 4 | -0.5012505 | 0.2529005 | 0.0502678 |
| climate.guilt | 5 | 239 | 2.921548 | 0.9676306 | 3.00 | 2.965026 | 1.11195 | 1 | 5 | 4 | -0.2744229 | -0.6667040 | 0.0625908 |
| climate.isolation | 6 | 239 | 2.821130 | 0.9430547 | 3.00 | 2.818653 | 1.11195 | 1 | 5 | 4 | 0.0587578 | -0.6621222 | 0.0610011 |
| climate.anxiety | 7 | 239 | 3.610879 | 0.9403127 | 4.00 | 3.693005 | 0.74130 | 1 | 5 | 4 | -0.8687992 | 0.1356662 | 0.0608238 |
| climate.sorrow | 8 | 239 | 4.064854 | 0.8417164 | 4.25 | 4.183938 | 0.74130 | 1 | 5 | 4 | -1.2282603 | 1.3711738 | 0.0544461 |
## $climate.anger
##
## Shapiro-Wilk normality test
##
## data: X[[i]]
## W = 0.92004, p-value = 0.0000000004786
##
##
## $climate.contempt
##
## Shapiro-Wilk normality test
##
## data: X[[i]]
## W = 0.94316, p-value = 0.00000005074
##
##
## $climate.enthusiasm
##
## Shapiro-Wilk normality test
##
## data: X[[i]]
## W = 0.94184, p-value = 0.00000003779
##
##
## $climate.powerlessness
##
## Shapiro-Wilk normality test
##
## data: X[[i]]
## W = 0.96874, p-value = 0.00004144
##
##
## $climate.guilt
##
## Shapiro-Wilk normality test
##
## data: X[[i]]
## W = 0.96677, p-value = 0.0000227
##
##
## $climate.isolation
##
## Shapiro-Wilk normality test
##
## data: X[[i]]
## W = 0.98014, p-value = 0.001968
##
##
## $climate.anxiety
##
## Shapiro-Wilk normality test
##
## data: X[[i]]
## W = 0.91516, p-value = 0.0000000002001
##
##
## $climate.sorrow
##
## Shapiro-Wilk normality test
##
## data: X[[i]]
## W = 0.88163, p-value = 0.000000000001055
We inspect the internal consistencies of the ICE scales using Cronbach’s alpha coefficient
| anger | contempt | enthusiasm | powerlessness | guilt | isolation | anxiety | sorrow | |
|---|---|---|---|---|---|---|---|---|
| raw_alpha | 0.892050814049292 | 0.857519620165384 | 0.858242392709822 | 0.679712971588263 | 0.882768915185896 | 0.845797968067275 | 0.899176100707087 | 0.899513338978477 |
| std.alpha | 0.890417086788687 | 0.857609797012417 | 0.858336050474642 | 0.67831814854768 | 0.881891491107175 | 0.845717534193633 | 0.899190341256113 | 0.899953324857259 |
| vars | n | mean | sd | median | trimmed | mad | min | max | range | skew | kurtosis | se | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| emo_wb | 1 | 239 | 3.948396 | 1.237689 | 4.0 | 4.008636 | 1.48260 | 1.000000 | 6 | 5.000000 | -0.4163687 | -0.6932443 | 0.0800594 |
| soc_wb | 2 | 239 | 3.195816 | 1.153649 | 3.2 | 3.180311 | 1.18608 | 1.000000 | 6 | 5.000000 | 0.0566383 | -0.7028915 | 0.0746234 |
| psy_wb | 3 | 239 | 3.992329 | 1.061842 | 4.0 | 4.034542 | 0.98840 | 1.166667 | 6 | 4.833333 | -0.3889437 | -0.3331321 | 0.0686849 |
## $emo_wb
##
## Shapiro-Wilk normality test
##
## data: X[[i]]
## W = 0.96002, p-value = 0.000003284
##
##
## $soc_wb
##
## Shapiro-Wilk normality test
##
## data: X[[i]]
## W = 0.98059, p-value = 0.002326
##
##
## $psy_wb
##
## Shapiro-Wilk normality test
##
## data: X[[i]]
## W = 0.97851, p-value = 0.001083
We inspect the internal consistencies of the MHC scales using Cronbach’s alpha coefficient
| emo_wb | soc_wb | psy_wb | |
|---|---|---|---|
| raw_alpha | 0.855735096094993 | 0.846650878994999 | 0.860443041538284 |
| std.alpha | 0.855753354854176 | 0.848789196268397 | 0.860883174998539 |
| vars | n | mean | sd | median | trimmed | mad | min | max | range | skew | kurtosis | se | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| cc_concern | 1 | 239 | 3.4225941 | 1.0578200 | 4 | 3.4870466 | 1.4826 | 1 | 5 | 4 | -0.5608707 | -0.1254629 | 0.0684247 |
| age | 2 | 239 | 46.5690377 | 15.8271401 | 46 | 46.4974093 | 20.7564 | 19 | 74 | 55 | 0.0404981 | -1.2454797 | 1.0237726 |
| gender | 3 | 239 | 0.5355649 | 0.4997802 | 1 | 0.5440415 | 0.0000 | 0 | 1 | 1 | -0.1417265 | -1.9881799 | 0.0323281 |
##
## Shapiro-Wilk normality test
##
## data: demographics$cc_concern
## W = 0.88709, p-value = 0.000000000002299
##
## Shapiro-Wilk normality test
##
## data: demographics$age
## W = 0.95016, p-value = 0.0000002598
Let’s use the Spearman correlation coefficient because the data is ordinal and non-parametrically distributed
| climate.anger | climate.contempt | climate.enthusiasm | climate.powerlessness | climate.guilt | climate.isolation | climate.anxiety | climate.sorrow | emo_wb | soc_wb | psy_wb | cc_concern | age | gender | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| climate.anger | ||||||||||||||
| climate.contempt | -0.63*** | |||||||||||||
| climate.enthusiasm | 0.44*** | -0.38*** | ||||||||||||
| climate.powerlessness | 0.33*** | -0.16* | 0.16* | |||||||||||
| climate.guilt | 0.46*** | -0.40*** | 0.37*** | 0.53*** | ||||||||||
| climate.isolation | 0.47*** | -0.18** | 0.37*** | 0.41*** | 0.49*** | |||||||||
| climate.anxiety | 0.72*** | -0.57*** | 0.48*** | 0.44*** | 0.60*** | 0.55*** | ||||||||
| climate.sorrow | 0.75*** | -0.62*** | 0.42*** | 0.34*** | 0.45*** | 0.44*** | 0.74*** | |||||||
| emo_wb | 0.12 | -0.10 | 0.25*** | 0.01 | 0.04 | 0.05 | 0.17** | 0.10 | ||||||
| soc_wb | 0.16* | -0.15* | 0.40*** | 0.09 | 0.15* | 0.22*** | 0.27*** | 0.15* | 0.64*** | |||||
| psy_wb | 0.18** | -0.14* | 0.35*** | 0.02 | 0.02 | 0.12 | 0.19** | 0.17** | 0.78*** | 0.76*** | ||||
| cc_concern | 0.74*** | -0.63*** | 0.53*** | 0.31*** | 0.60*** | 0.49*** | 0.76*** | 0.72*** | 0.17** | 0.27*** | 0.24*** | |||
| age | 0.07 | 0.01 | 0.13 | 0.05 | 0.03 | 0.21** | 0.02 | 0.14* | 0.19** | 0.27*** | 0.31*** | 0.10 | ||
| gender | 0.03 | 0.08 | -0.08 | -0.13* | -0.29*** | -0.08 | -0.08 | -0.12 | 0.02 | 0.06 | 0.06 | -0.09 | 0.03 |
The multiple linear regression model with climate enthusiasm, climate anxiety, age, and climate concern as predictors
##
## Call:
## lm(formula = emo_wb ~ climate.enthusiasm + climate.anxiety +
## age + cc_concern, data = df_wb)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.9023 -0.7704 0.1735 0.8760 2.5495
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.099623 0.421550 4.981 0.00000123 ***
## climate.enthusiasm 0.240779 0.120580 1.997 0.0470 *
## climate.anxiety 0.060717 0.142655 0.426 0.6708
## age 0.012700 0.004947 2.568 0.0109 *
## cc_concern 0.050088 0.126989 0.394 0.6936
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.195 on 234 degrees of freedom
## Multiple R-squared: 0.08286, Adjusted R-squared: 0.06718
## F-statistic: 5.285 on 4 and 234 DF, p-value: 0.0004307
Standardised regressions coefficients
##
## Call:
## lm(formula = emo_wb ~ climate.enthusiasm + climate.anxiety +
## age + cc_concern, data = df_wb)
##
## Standardized Coefficients::
## (Intercept) climate.enthusiasm climate.anxiety age cc_concern
## NA 0.15892076 0.04612844 0.16240951 0.04280899
Squared Semi-partial correlation coefficients
## Predictor 1: semi partial = 0.126; squared semipartial = 0.016
## Predictor 2: semi partial = 0.032; squared semipartial = 0.001
## Predictor 3: semi partial = 0.161; squared semipartial = 0.026
## Predictor 4: semi partial = 0.032; squared semipartial = 0.001
95% bootstrapped and accelerated confidence intervals of the regression coefficients
For the intercept
## BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS
## Based on 1000 bootstrap replicates
##
## CALL :
## boot.ci(boot.out = results, type = "bca", index = 1)
##
## Intervals :
## Level BCa
## 95% ( 1.337, 2.928 )
## Calculations and Intervals on Original Scale
For climate enthusiasm
## BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS
## Based on 1000 bootstrap replicates
##
## CALL :
## boot.ci(boot.out = results, type = "bca", index = 2)
##
## Intervals :
## Level BCa
## 95% ( 0.0375, 0.5061 )
## Calculations and Intervals on Original Scale
For climate anxiety
## BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS
## Based on 1000 bootstrap replicates
##
## CALL :
## boot.ci(boot.out = results, type = "bca", index = 3)
##
## Intervals :
## Level BCa
## 95% (-0.2191, 0.3631 )
## Calculations and Intervals on Original Scale
For age
## BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS
## Based on 1000 bootstrap replicates
##
## CALL :
## boot.ci(boot.out = results, type = "bca", index = 4)
##
## Intervals :
## Level BCa
## 95% ( 0.0026, 0.0224 )
## Calculations and Intervals on Original Scale
For climate concern
## BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS
## Based on 1000 bootstrap replicates
##
## CALL :
## boot.ci(boot.out = results, type = "bca", index = 5)
##
## Intervals :
## Level BCa
## 95% (-0.2284, 0.3673 )
## Calculations and Intervals on Original Scale
Assumptions check
Let’s have a look at the plots of model residuals:
We know from the correlation matrix that correlations of the independent variables do not exceed the customary cutoff point of .8 so we can say that this assumption is met. But let’s also have a look at the Variance Inflation Factor:
## climate.enthusiasm climate.anxiety age cc_concern
## 1.616067 2.996910 1.020857 3.005494
The value for VIF starts at 1 and has no upper limit. A general rule of thumb for interpreting VIFs is as follows:
Let’s check the plot of the predicted values against the standardized residual values from point 1 to confirm that the points are equally distributed across all the values of the independent variables.
The multiple linear regression model with climate anger, climate contempt, climate enthusiasm, climate anxiety, climate sorrow, age, and climate concern as predictors
##
## Call:
## lm(formula = psy_wb ~ climate.anger + climate.contempt + climate.enthusiasm +
## climate.anxiety + climate.sorrow + age + cc_concern, data = df_wb)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.88287 -0.55304 0.07385 0.63997 2.61958
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.807346 0.663710 2.723 0.006961 **
## climate.anger 0.041766 0.123242 0.339 0.734998
## climate.contempt 0.003068 0.096898 0.032 0.974772
## climate.enthusiasm 0.375773 0.099179 3.789 0.000193 ***
## climate.anxiety -0.017045 0.127154 -0.134 0.893477
## climate.sorrow -0.044280 0.139507 -0.317 0.751226
## age 0.017670 0.004119 4.290 0.0000262 ***
## cc_concern 0.024231 0.120680 0.201 0.841041
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9797 on 231 degrees of freedom
## Multiple R-squared: 0.1737, Adjusted R-squared: 0.1487
## F-statistic: 6.938 on 7 and 231 DF, p-value: 0.0000001675
Standardised regressions coefficients
##
## Call:
## lm(formula = psy_wb ~ climate.anger + climate.contempt + climate.enthusiasm +
## climate.anxiety + climate.sorrow + age + cc_concern, data = df_wb)
##
## Standardized Coefficients::
## (Intercept) climate.anger climate.contempt climate.enthusiasm climate.anxiety climate.sorrow
## NA 0.036976222 0.002916741 0.289094390 -0.015094541 -0.035100284
## age cc_concern
## 0.263380391 0.024139296
Squared Semi-partial correlation coefficients
## Predictor 1: semi partial = 0; squared semipartial = 0
## Predictor 2: semi partial = 0; squared semipartial = 0
## Predictor 3: semi partial = 0.226; squared semipartial = 0.051
## Predictor 4: semi partial = 0; squared semipartial = 0
## Predictor 5: semi partial = 0; squared semipartial = 0
## Predictor 6: semi partial = 0.257; squared semipartial = 0.066
## Predictor 7: semi partial = 0; squared semipartial = 0
95% bootstrapped and accelerated confidence intervals of the regression coefficients
For the intercept
## BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS
## Based on 1000 bootstrap replicates
##
## CALL :
## boot.ci(boot.out = results, type = "bca", index = 1)
##
## Intervals :
## Level BCa
## 95% ( 0.443, 3.290 )
## Calculations and Intervals on Original Scale
For climate anger
## BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS
## Based on 1000 bootstrap replicates
##
## CALL :
## boot.ci(boot.out = results, type = "bca", index = 2)
##
## Intervals :
## Level BCa
## 95% (-0.2301, 0.3596 )
## Calculations and Intervals on Original Scale
For climate contempt
## BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS
## Based on 1000 bootstrap replicates
##
## CALL :
## boot.ci(boot.out = results, type = "bca", index = 3)
##
## Intervals :
## Level BCa
## 95% (-0.1972, 0.2040 )
## Calculations and Intervals on Original Scale
For climate enthusiasm
## BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS
## Based on 1000 bootstrap replicates
##
## CALL :
## boot.ci(boot.out = results, type = "bca", index = 4)
##
## Intervals :
## Level BCa
## 95% ( 0.1850, 0.5687 )
## Calculations and Intervals on Original Scale
For climate anxiety
## BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS
## Based on 1000 bootstrap replicates
##
## CALL :
## boot.ci(boot.out = results, type = "bca", index = 5)
##
## Intervals :
## Level BCa
## 95% (-0.2876, 0.2340 )
## Calculations and Intervals on Original Scale
For climate sorrow
## BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS
## Based on 1000 bootstrap replicates
##
## CALL :
## boot.ci(boot.out = results, type = "bca", index = 6)
##
## Intervals :
## Level BCa
## 95% (-0.3272, 0.2545 )
## Calculations and Intervals on Original Scale
For age
## BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS
## Based on 1000 bootstrap replicates
##
## CALL :
## boot.ci(boot.out = results, type = "bca", index = 7)
##
## Intervals :
## Level BCa
## 95% ( 0.0102, 0.0259 )
## Calculations and Intervals on Original Scale
For climate concern
## BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS
## Based on 1000 bootstrap replicates
##
## CALL :
## boot.ci(boot.out = results, type = "bca", index = 8)
##
## Intervals :
## Level BCa
## 95% (-0.2773, 0.2750 )
## Calculations and Intervals on Original Scale
Assumptions check
Let’s have a look at the plots of model residuals:
We know from the correlation matrix that correlations of the independent variables do not exceed the customary cutoff point of .8 so we can say that this assumption is met. But let’s also have a look at the Variance Inflation Factor:
## climate.anger climate.contempt climate.enthusiasm climate.anxiety climate.sorrow age
## 3.328144 2.373049 1.627626 3.544578 3.418913 1.053609
## cc_concern
## 4.040679
The value for VIF starts at 1 and has no upper limit. A general rule of thumb for interpreting VIFs is as follows:
Let’s check the plot of the predicted values against the standardized residual values from point 1 to confirm that the points are equally distributed across all the values of the independent variables.
This HTML output presents the general logic of the analysis along with some results not outlined in the main body of the manuscript. Please note that the full R code for the data cleaning and data analysis is available in the supplementary materials on the accompanying OSF website.
Social wellbeing
The multiple linear regression model with climate anger, climate contempt, climate enthusiasm, climate guilt, climate isolation, climate anxiety, climate sorrow , age, and climate concern as predictors
Standardised regressions coefficients
Squared Semi-partial correlation coefficients
Plotting the relationship between social wellbeing, enthusiasm and anxiety
95% bootstrapped and accelerated confidence intervals of the regression coefficients
For the intercept
For climate anger
For climate contempt
For climate enthusiasm
For climate guilt
For climate isolation
For climate anxiety
For climate sorrow
For age
For climate concern
Assumptions check
1. Distribution of the model residuals
Let’s have a look at the plots of model residuals:
2. Linear relationship between independent and dependent variables
3. No multicollinearity
We know from the correlation matrix that correlations of the independent variables do not exceed the customary cutoff point of .8 so we can say that this assumption is met. But let’s also have a look at the Variance Inflation Factor:
The value for VIF starts at 1 and has no upper limit. A general rule of thumb for interpreting VIFs is as follows:
4. Homoscedasticity
Let’s check the plot of the predicted values against the standardized residual values from point 1 to confirm that the points are equally distributed across all the values of the independent variables.